Multiple camera system for obtaining high resolution images of objects

ABSTRACT

A system and corresponding method for image acquisition are provided, the system including a processor, an imaging adapter in signal communication with the processor for receiving image data from each of a static imaging device and a dynamic imaging device, and a homography unit in signal communication with the processor for computing a planar homography between the static and dynamic image data; and the method including receiving an image from a static imaging device, receiving an image from a dynamic imaging device, and registering the dynamic image to the static image using planar homography.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application Ser.No. 60/540,546 (Attorney Docket No. 2004P01461 US), filed Jan. 30, 2004and entitled “A Multiple Camera System for Obtaining High ResolutionImages of Objects”, which is incorporated herein by reference in itsentirety.

BACKGROUND

With the increased importance of securing many medium-to-large-scalesites, the use of video cameras to obtain information about theactivities in the site is typical. In many such sites, personnel monitormultiple video streams, and they may also manually control dynamicpan-tilt-zoom (PTZ) cameras to obtain more detailed information aboutthe observed objects of interest.

Multi-camera vision systems have been developed using a wide range ofcamera arrangements for various purposes. For better stereo matching,some systems use closely spaced cameras. Others adopt the oppositearrangement of widely separated cameras for maximum visibility. Some mayuse a hybrid approach. Still others use multiple cameras for the mainpurpose of increasing the field of view. Various methods for findingplanar correspondences across cameras have also been suggested.

Accordingly, what is desired is a system and method for detectingobjects of interest and controlling dynamic cameras to obtained detailedviews of objects.

SUMMARY

These and other drawbacks and disadvantages of the prior art areaddressed by a multiple camera system and method for obtaininghigh-resolution images of objects.

A system for image acquisition is provided, including a processor, animaging adapter in signal communication with the processor for receivingimage data from each of a static imaging device and a dynamic imagingdevice, and a homography unit in signal communication with the processorfor computing a planar homography between the static and dynamic imagedata

A corresponding method for image acquisition is provided, the methodincluding receiving an image from a static imaging device, receiving animage from a dynamic imaging device, and registering the dynamic imageto the static image using a planar homography.

These and other aspects, features and advantages of the presentdisclosure will become apparent from the following description ofexemplary embodiments, which is to be read in connection with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure teaches a multiple camera system and method forobtaining high-resolution images of objects in accordance with thefollowing exemplary figures, in which:

FIG. 1 shows a schematic diagram of a system for obtaininghigh-resolution images of objects in accordance with an illustrativeembodiment of the present disclosure;

FIG. 2 shows a flow diagram of a method for obtaining high-resolutionimages of objects in accordance with an illustrative embodiment of thepresent disclosure;

FIG. 3 shows an image diagram with schematic overlay of regions for theuse of dynamic programming in accordance with an illustrative embodimentof the present disclosure;

FIG. 4 shows a schematic diagram of a decision tree for scheduling inaccordance with an illustrative embodiment of the present disclosure;and

FIG. 5 shows an image sequence diagram with static/dynamic image pairsin accordance with an illustrative embodiment of the present disclosure.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

An exemplary system embodiment of the present disclosure utilizesmultiple cameras, including static cameras as well as dynamicpan-tilt-zoom (PTZ) cameras for obtaining high-resolution images ofobjects. In the exemplary system, static cameras, each with a wide fieldof view, are used to obtain high-level information about a large part ofa scene. Such cameras may be placed to look at different parts of thescene, or at the same scene from different viewpoints to handleocclusion problems due to trees, poles and the like. Object detectionmay be performed in these views using standard background subtractiontechniques. This information is then used by the exemplary system tocontrol the moving PTZ cameras to aim at detected objects of interest.Such information transfer across cameras is performed using planarhomographies. The homographies enable the transfer of points lying on aplane.

Since outdoor scenes generally have a dominant ground plane or set ofplanes, such homography-based point transfer is feasible for mostoutdoor scenes. When multiple objects of interest are present in thescene and one or multiple PTZ cameras are available, an optimizationprocedure is utilized to schedule or control the motion of the PTZcameras such that information about the maximum number of objects ofinterest in the scene is captured by at least one of the dynamiccameras. Such scheduling utilizes learned information about objecttrajectories so that the objects that are expected to disappear soonerare captured before objects that are expected to be visible for a longerperiod of time.

As shown in FIG. 1, a multiple camera system for obtaininghigh-resolution images of objects, according to an illustrativeembodiment of the present disclosure, is indicated generally by thereference numeral 100. The system 100 includes at least one processor orcentral processing unit (CPU) 102 in signal communication with a systembus 104. A read only memory (ROM) 106, a random access memory (RAM) 108,a display adapter 110, an I/O adapter 112, a user interface adapter 114,a communications adapter 128, and an imaging adapter 130 are also insignal communication with the system bus 104. A display unit 116 is insignal communication with the system bus 104 via the display adapter110. A disk storage unit 118, such as, for example, a magnetic oroptical disk storage unit is in signal communication with the system bus104 via the I/O adapter 112. A mouse 120, a keyboard 122, and an eyetracking device 124 are in signal communication with the system bus 104via the user interface adapter 114. A static imaging device 132 is insignal communication with the system bus 104 via the imaging adapter130, and a dynamic imaging device 134 is in signal communication withthe system bus 104 via the imaging adapter 130.

A homography unit 172 and a trajectory unit 180 are also included in thesystem 100 and in signal communication with the CPU 102 and the systembus 104. While the homography unit 172 and the trajectory unit 180 areillustrated as coupled to the at least one processor or CPU 102, thesecomponents are preferably embodied in computer program code stored in atleast one of the memories 106, 108 and 118, wherein the computer programcode is executed by the CPU 102.

As will be recognized by those of ordinary skill in the pertinent artbased on the teachings herein, alternate embodiments are possible, suchas, for example, embodying some or all of the computer program code inregisters located on the processor chip 102. Given the teachings of thedisclosure provided herein, those of ordinary skill in the pertinent artwill contemplate various alternate configurations and implementations ofthe homography unit 172 and the trajectory unit 180, as well as theother elements of the system 100, while practicing within the scope andspirit of the present disclosure.

Turning to FIG. 2, a flowchart for obtaining high-resolution images ofobjects, according to an illustrative embodiment of the presentdisclosure, is indicated generally by the reference numeral 200. Theflowchart 200 includes a start block 210 that passes control to an inputblock 212. The input block 212 receives static image data from a fixedcamera and passes control to an input block 214. The input block 214receives static image data from a dynamic camera and passes control to afunction block 216.

The function block 216 computes a first planar homography for the staticimage data and passes control to an input block 218. The input block 218receives dynamic image data from the dynamic camera, and passes controlto a function block 220. The function block 220 computes a second planarhomography between the static and dynamic images from the dynamiccamera, and passes control to a function block 222. The function block222 registers the dynamic image to the fixed image in correspondencewith the first and second computed homographies, and passes control to afunction block 224. The function block 224, in turn, detects objects ofinterest and passes control to a function block 226. The function block226 projects trajectories of the objects of interest in response to theregistered image data, and passes control to a function block 228. Thefunction block 228 schedules or controls the dynamic imaging of objectsin response to the object trajectories, and passes control back to theinput block 218 for receiving more dynamic image data. One or more ofthe function blocks 216, 220, 222, 224, 226 and 228 may use supplementaldata obtained at different points in time from the current dynamic imagedata.

Turning now to FIG. 3, an individual image is indicated generally by thereference numeral 300, with regions 310 and 312 showing the use ofdynamic programming to efficiently compute summations in the projectionregions. The two adjacent regions 310 and 312 have overlapping area, andgiven the result for one, the result for the other can be computedefficiently.

As shown in FIG. 4, a schematic decision tree for a scheduling problemis indicated generally by the reference numeral 400. The a_(i) termsrepresent control actions by the system, and refer to scheduling aparticular camera to a particular target. The e_(i) terms representevents in the scene and refer to one out of the many random events thatcan occur during the time the control action is taken.

Turning to FIG. 5, image results from the system are indicated generallyby the reference numeral 500. The image results are shown here in pairs,with an image from the static camera displayed on the left side and acorresponding image from the dynamic camera displayed on the right sideof each pair. That is, the right-hand side image is the high-resolutionimage captured by the PTZ camera. As such, the image pair 514, forexample, includes a static image 512 and a dynamic image 514. Likewise,each of the other image pairs 524 through 584 includes a static imageand a dynamic image. The system creates the time-stamped summary videosthat selectively store all of the frames that have some activityoccurring. Here, one time-stamped summary video taken in snowy weatherincludes the image pairs 514 through 554, while another time-stampedsummary video taken in clear weather includes the image pairs 564through 584. This can be used as a tool by security personnel to checkpast activities occurring during a given period of time, such as, forexample, to determine if and when an anomalous activity occurred in aregion and to capture high-resolution images of such activitiesautomatically.

In operation, the exemplary embodiment system receives image data fromstatic and dynamic cameras. In addition, the dynamic cameras may beutilized as static cameras by positioning them back to their defaultwide field-of-view position when there is no event of interest. Thestatic cameras with a wide field of view and/or dynamic cameras in theirstatic state are utilized to obtain high-level information about a largepart of the scene. Such cameras may be placed to look at different partsof the scene, or at the same scene from different viewpoints to handleocclusion problems due to trees, poles and the like.

Object detection is performed in these views using backgroundsubtraction techniques. In addition, the system exchanges informationbetween cameras to track objects across them. When the cameras have someoverlapping area, such transfer is eased based on common appearances ata particular location. When the overlapping area is non-existent, morecomplex matching techniques are utilized based on circumstantial objectappearance and shape characteristics. Information about the sceneobtained from static cameras is used by the system to control the movingcameras to aim at detected objects of interest. Such informationtransfer across cameras is performed using planar homographies, whichare able to transfer points lying on a plane.

Object detection is primarily accomplished using static cameras. Thestatic cameras are preferably utilized for detecting objects of interestin the scene. Several methods are possible for change detection. Themost popular methods are the Mixture-of-Gaussians model based methodsand the non-parametric kernel based methods. The output from such methodmodules is a pixel-level detection measure that measures the probabilitythat the pixel belongs to the background.

Object size priors are used for robust change detection. Informationfrom the pixel-level detector is used for making higher-level decisionsabout object presence in the scene. Given the shape of the objectsought, it is possible to obtain an approximate projection onto theimage that the object will form. Objects that are closer appear largerthan distant objects. If people are approximated by cylinders, suchprojection is invariant to the pose of the person. This, however, is nottrue for other objects such as cars, for example. Given suchprojections, a projection region is created that would be formed if theobject was standing at that location, for each point on the groundplane. The system uses the information that an object presence at thislocation would lead to most of the pixels detecting change in theprojection region. A threshold can then be set on the number of “on”pixels in this projection region.

Determining the number of “on” pixels in the projection regions of allpossible points on the ground plane can be an expensive process ifperformed iteratively. Therefore, preferred embodiments use dynamicprogramming to efficiently find the “summations” for all of theprojection regions. The essential idea is that the projection regionsfor consecutive points are generally of the same or similar sizes, butdiffer in their positions.

The summation is found in two steps. In one step, a horizontal summationis performed that sums the relevant pixels in the horizontal direction.In the other step, the summation is performed in the vertical direction.This is done if the horizontal summation regions do not changedrastically with the change in the vertical region.

In order to perform the horizontal summation, it is observed that giventhe projection region for a location to the left of the currentlocation, the projection region for the current location has one extrapoint on the right and one point less point on the left. Thus,determination of the summation for the current location involves oneaddition of the extra point and one subtraction of the redundant point.This can be performed very efficiently. A similar procedure in thevertical direction on the horizontal summations yields the complete 2Dsummation extremely efficiently.

Filtering via motion detection may be optionally implemented. Thedetections obtained via the above procedure can be further verified bytracking an object once it is initially detected. Many methods may beused for the purpose. Using this procedure, only objects that aresuccessfully tracked for some time are counted as detected, while therest are filtered out.

Information fusion may be accomplished between different static cameras.When some overlap exists between the views of different cameras, aground-plane homography may be established for correspondence acrossthem. This homography may be utilized for identifying common objectsdetected in multiple views.

Determining the control parameters of the PTZ cameras involves theaspects of transferring information across cameras, and predicting thenew object position given the motion characteristics of the object.

In order to transfer point information from the static cameras to thePTZ cameras, a zoomed-out image is obtained from a PTZ camera and ahomography correspondence is established between this view and thestatic cameras for the ground plane. A point in the static camera isfirst transferred to this view via the homography. Then, given the panand tilt position of this reference view and the focal length of thelens in this position, the system can then determine the pan and tiltangles of the point with respect to the camera coordinate. Any two viewsfrom a PTZ camera having PTZ parameters such as pan angle theta_(i),tilt angle phi_(i), and focal length f_(i) corresponding to zoom, arerelated by a homography H as indicated by Equations 1 through 3 of Table1, where f_(x) and f_(y) are the focal lengths in the x and ydirections, s is the skew and u_(x) and u_(y) are the locationcoordinates of the principal point in the image, and subject toEquations 4 and 5 of Table 1.

This transformation x₂=H·x₁ is first used to transfer the point from thecurrent PTZ location to a location that has a tilt of zero. From a zerotilt position, the pan-tilt position to point the camera to this pointbecomes straightforward as indicated by Equations 6 and 7 of Table 1.These equations were derived using simple geometric considerations.These pan and tilt angles can then be used to point the camera at thedesired point.

Predicting object location in a dynamic environment involves trackingthe object position changes in the scene. Thus, during the time that thecamera takes to go to the desired location, the object position changes.Such change necessitates prediction of the object position at the timethe camera is predicted to move to the new location. Two methods arepresented for prediction.

In the first method, a linear prediction uses the velocity of the objectin order to predict the object position after the estimated cameracontrol time delay. Such linear prediction can either be a simple schemebased on estimating the velocity using differences in object position,or can be slightly more complicated using a Kalman filter.

The linear prediction approach works quite well. However, it may fallshort when the object undergoes a change from linear behavior, such asduring acceleration. Such behavior is typical at a road intersection,for example. In order to improve upon the linear prediction mechanism inacceleration environments, a second approach learns the motion patternsof activities. Then, for each location and velocity of the object, themost probable location is predicted where the object should be observedafter the expected control delay. The predicted location is thentransferred to the PTZ camera for capture.

The zoom to be applied to the object should be such that it captures theobject property of interest with the maximum possible zoom with theprobability of losing the object not exceeding a given threshold. Forinstance, if one needs to capture a whole object such as a car, oneneeds to take the following into account:

1. Object Size: Larger objects need to be captured with lesser zoom ascompared to smaller objects.

2. Error in object position localization: A larger error in thelocalization of the object position necessitates a lower zoom setting.

3. Prediction Error: A larger error in prediction of the object motionagain necessitates a lower zoom setting

4. Transfer Error: Since only the points on the plane can betransferred, and the actual point that is transferred may not be on theplane, there is a localization error that appears due to the transferprocess via the homography.

All of these errors can be quantized. First, the error in finding thepoint to be transferred needs to be determined in the original image.Typically, it is preferable to transfer the middle bottom point of theobject. It may be assumed that the error in locating this point can beas much as sigma² _(x)=width²/4, delta² _(y)=height²/4, where the widthand height of the detected object are used. In addition, the predictionerror is estimated as a fixed constant of the object velocity sigma²_(pred)X=c_(p)·v. Given such errors, the error in localization of thepoint in the other image can be determined. Given the homographytransformation as indicated by Equation 8 of Table 1, the error in the xand y coordinates (x₂, y₂) in the second image can be estimated asindicated by Equations 9 and 10 of Table 1.

Once the point has been transferred and the error in the transfer hasbeen estimated, one needs to localize the object characteristic thatneeds to be captured. For instance, if the license number plate is to becaptured, it has to first be localized in the view of the other camera.Given the variability in the number plate positions, such error can beestimated as a fixed error Σ₂.

All these errors in localization can be added to determine the totalerror in localization of the object in the reference view of the PTZcamera. Such errors translate into an angular uncertainty for the PTZcamera as indicated by Equation 11 of Table 1, where alpha is the anglefrom the principal axis to the transferred point (x₂, y₂). Such error inthe angular orientation is then translated into the maximum zoom factorthat still facilitates at least this much angular coverage.

When multiple objects are present in the scene, the cameras need to bescheduled so that a given criteria regarding the captured objects issatisfied. For instance, one may desire that the maximum number ofobjects be captured by the system. Another criteria could be to capturethe largest object in the scene.

A generic framework is now provided for scheduling the cameras, given acertain criteria to be maximized. In order to do so, the motion patternof the objects is learned. Given a particular location and velocity ofthe object, the future location of the object is determined. Since theobjects can have multiple patterns of motion, multiple locations aredetermined with their respective frequencies or probabilities. Alsodetermined is the estimated time for which the object remains visibleafter the current observation.

Given the learned model, a technique is used to maximize a givencriteria for determining the optimum scheduling mechanism. Assume fornow that the criteria is to maximize the number of captured objects, orequivalently, to minimize the number of uncaptured objects. In addition,assume that the objects appear randomly according to a givendistribution, and disappear at specific locations at specific timesaccording to the learned distribution. It is further assumed that acompound event e_(i), where i=1 . . . n occurs in the scene randomlyaccording to the given distribution. Then, let A=a_(i), where i=1 . . .tau be a sequence of control actions taken by the system. Then, theproblem can be formulated as indicated by Equation 12 of Table 1. Thatis, the optimum control strategy is the one that minimizes the averagenumber of lost objects given a certain expected distribution for futureevents e_(i).

Such a formulation would be optimal when there is no feedback from thesystem during the control process. Since there is constant feedback fromthe system, the formulation is modified into an incremental one. In anincremental formulation, one can construct a probabilistic expectimindecision tree as shown in FIG. 4 that captures the decision process. Theidea is that at any given instant there is a set of control actions fromwhich the system can choose. In the exemplary application, such controlactions refer to pointing the PTZ camera towards a particular object inthe scene. Such action has some time “cost” associated with it. The timecost includes the time taken by the camera to move to the new position.Such time is a function of the current position and the new position ofthe camera, and typically increases as the difference between the twoincreases.

The time cost also includes the time to capture the object. This is afunction of the uncertainty in capturing the object. For instance,objects that are moving fast will typically have more uncertainty intheir position as compared to stationary ones. Therefore, even when thecamera moves to a predicted position, the probability that it hascaptured the object is low. On the other hand, slowly moving objects canbe localized very accurately. Furthermore, if a certain property of theobject is to be acquired, such as vehicle license plates, for example,there is uncertainty in the localization of the location to be acquired.Large objects like trucks or sport utility vehicles can have theirnumber plates in very high and/or unpredictable locations. In suchcases, multiple images may need to be acquired so that the probabilitythat the plate has been captured in at least one of the views exceeds agiven threshold. Such factors are included so as to determine theexpected time that a particular control action will take.

After such action is performed, the current configuration of the scenemay change randomly. For instance, some of the objects could have goneout of the field of view of the camera. Some new objects may haveentered the scene. In addition, some of the characteristics of existingobjects will have changed. All of these changes can be incorporated into a single compound event e_(i) that can occur with probability p_(i).Associated with this event is also a loss I_(i) that occurs because ofthe loss of objects that have left the scene uncaptured. The total lossL can be calculated as indicated by Equations 13 and 14 of Table 1

L(If_(ei)) is the loss in the leaf node of e_(i) and is the expectedloss that occurs after the event e_(i) has taken place. In other words,the loss is computed for each possible event and the expected loss iscalculated as the weighted sum of such losses with the weight being theexpectation of the event. For each such event, the node is then againexpanded to determine the expected loss occurring after the event. Thisprocess can be quite computationally expensive, especially when thenumber of objects is large. Therefore, the depth up to which this iscomputed is adapted to the number of objects in the scene. When multiplePTZ cameras are available, such a procedure can be extended byperforming the computation after any of the cameras finishes its task.

As described, the exemplary embodiment system can be an important toolin automating monitoring and surveillance tasks in major and minor sitesincluding parking lots, railway stations, subway stations, airports andfor security in areas such as military installations, museums, shoppingmalls and homes, for example.

The video summary feature discussed with respect to FIG. 5 can beimportant in summarizing events during a given period of time, and henceautomatically achieving great efficiency in storing video data, therebyenhancing the amount of useful data that can be stored for the samestorage capacity. Furthermore, high resolution images of objects can beutilized for detailed object acquisition that can form the basis formore complex reasoning about objects such as recognition, camerahandoff, video indexing, smart object search, and the like. One suchapplication is to acquire images containing license plate numbers ofcars, and then automatically reading the plates. This can then beutilized for automatic vehicle recognition, for example.

The features and methods provided may be adapted to a variety ofapplications where the high-resolution images of objects need to beobtained. For instance, in a law enforcement application, one mayutilize a dual-camera system for obtaining high-resolution images ofnumber plates that can then be fed into a character recognition enginefor license plate recognition. Other possible application areas includeindustrial automation, traffic monitoring, and the like, as well as fortasks such as plane docking in airports, industrial automation in avariety of scenarios, and vision-based robotic systems.

These and other features and advantages of the present disclosure may bereadily ascertained by one of ordinary skill in the pertinent art basedon the teachings herein. It is to be understood that the teachings ofthe present disclosure may be implemented in various forms of hardware,software, firmware, special purpose processors, or combinations thereof.

Most preferably, the teachings of the present disclosure are implementedas a combination of hardware and software. Moreover, the software ispreferably implemented as an application program tangibly embodied on aprogram storage unit. The application program may be uploaded to, andexecuted by, a machine comprising any suitable architecture. Preferably,the machine is implemented on a computer platform having hardware suchas one or more central processing units (CPU), a random access memory(RAM), and input/output (I/0) interfaces.

The computer platform may also include an operating system andmicroinstruction code. The various processes and functions describedherein may be either part of the microinstruction code or part of theapplication program, or any combination thereof, which may be executedby a CPU. In addition, various other peripheral units may be connectedto the computer platform such as an additional data storage unit and aprinting unit.

It is to be further understood that, because some of the constituentsystem components and methods depicted in the accompanying drawings arepreferably implemented in software, the actual connections between thesystem components or the process function blocks may differ dependingupon the manner in which the present disclosure is programmed. Given theteachings herein, one of ordinary skill in the pertinent art will beable to contemplate these and similar implementations or configurationsof the present disclosure.

Although the illustrative embodiments have been described herein withreference to the accompanying drawings, it is to be understood that thepresent disclosure is not limited to those precise embodiments, and thatvarious changes and modifications may be effected therein by one ofordinary skill in the pertinent art without departing from the scope orspirit of the present disclosure. All such changes and modifications areintended to be included within the scope of the present disclosure asset forth in the appended claims. TABLE 1 H = K₂ * R₂ * R₁ ⁻¹ * K₁ ⁻¹(Eqn. 1) where R_(i) = Rot(φ_(i)) * Rot(θ_(i)) (Eqn. 2) and$K_{i} = \begin{bmatrix}f_{z} & s & u_{z} \\0 & f_{y} & u_{y} \\0 & 0 & 1\end{bmatrix}$ (Eqn. 3) ${{Rot}(\theta)} = \begin{bmatrix}{\cos(\theta)} & 0 & {- {\sin(\theta)}} \\0 & 1 & 0 \\{\sin(\theta)} & 0 & {\cos(\theta)}\end{bmatrix}$ (Eqn. 4) and ${{Rot}(\phi)} = \begin{bmatrix}1 & 0 & 0 \\0 & {\cos(\phi)} & {\sin(\phi)} \\0 & {- {\sin(\phi)}} & {\cos(\phi)}\end{bmatrix}$ (Eqn. 5) ${\tan(\theta)} = \frac{x}{f_{z}}$ (Eqn. 6)${\tan(\phi)} = \frac{y}{f_{y}{\sec(\theta)}}$ (Eqn. 7)${H = \begin{bmatrix}h_{1} & h_{2} & h_{3} \\h_{4} & h_{5} & h_{6} \\h_{7} & h_{8} & h_{9}\end{bmatrix}},$ (Eqn. 8)$\sigma_{x_{2}}^{2} = {\frac{1}{\left( {{h_{y}x} + {h_{8}y} + h_{9}} \right)}\left( {\left( {{h_{1}\sigma_{x}^{2}} + {h_{2}\sigma_{y}^{2}}} \right) +} \right.}$(Eqn. 9)$\left. {\left( \frac{{h_{1}x} + {h_{2}y} + h_{3}}{{h_{7}x} + {h_{8}y} + h_{9}} \right)\left( {{h_{7}\sigma_{x}^{2}} + {h_{8}\sigma_{y}^{2}}} \right)} \right)$$\sigma_{y_{2}}^{2} = {\frac{1}{\left( {{h_{y}x} + {h_{8}y} + h_{9}} \right)}\left( {\left( {{h_{4}\sigma_{x}^{2}} + {h_{5}\sigma_{y}^{2}}} \right) +} \right.}$(Eqn. 10)$\left. {\left( \frac{{h_{4}x} + {h_{5}y} + h_{6}}{{h_{7}x} + {h_{8}y} + h_{9}} \right)\left( {{h_{7}\sigma_{x}^{2}} + {h_{8}\sigma_{y}^{2}}} \right)} \right)$$\sigma_{a} = \frac{\sqrt{\left( {\sigma_{x_{2}}^{2} + \sigma_{y_{2}}^{2}} \right)}\cos^{2}\alpha}{f}$(Eqn. 11) A^(opt) = argmin_(A)P(e_(i))E(loss/e_(i), A) (Eqn. 12)$L = {\overset{\quad}{\min\limits_{i}\quad L}\left( a_{i} \right)}$(Eqn. 13) where${L\left( a_{i} \right)} = {\sum\limits_{i = 1}^{m}\quad{p_{i}\left( {l_{i} + {L\left( {lf}_{e_{i}} \right)}} \right)}}$(Eqn. 14)

1. A method of image acquisition comprising: receiving an image from astatic imaging device; receiving an image from a dynamic imaging device;and registering the dynamic image to the static image using planarhomography.
 2. A method as defined in claim 1 wherein the first andsecond imaging devices are different cameras, the step of registeringcomprising offline registration of the images received from thedifferent cameras using planar homographies.
 3. A method as defined inclaim 1 wherein the first and second imaging devices are differentcameras, further comprising transferring information between thedifferent cameras for geometric calibration of the cameras using planarhomographies.
 4. A method as defined in claim 1 wherein the first andsecond imaging devices are the same camera, the step of registeringcomprising online registration of the images received from the camerawhen the camera moves by utilization of the camera parameters.
 5. Amethod as defined in claim 1, further comprising: detecting an object ofinterest in at least one of the static and dynamic images; projecting atrajectory for the object of interest; and scheduling the dynamicimaging device in response to the trajectory.
 6. A method as defined inclaim 1, further comprising: detecting an object of interest in at leastone of the static and dynamic images; and controlling the dynamicimaging device in response to the object.
 7. A method as defined inclaim 1, further comprising: detecting an object of interest in at leastone of the static and dynamic images; and storing each of the static anddynamic images in association with a timestamp.
 8. A method as definedin claim 1, further comprising: detecting an object of interest in atleast one of the static and dynamic images; and controlling the dynamicimaging device in response to acquisition criteria.
 9. A method asdefined in claim 8 wherein the acquisition criteria includes the size ofthe desired objects of interest.
 10. A method as defined in claim 8wherein the acquisition criteria includes the acceleration of thedesired objects of interest.
 11. A method as defined in claim 1 whereinuser interaction is not required.
 12. An apparatus for image acquisitioncomprising: imaging means for receiving at least one static image;imaging means for receiving at least one dynamic image; and homographymeans for registering the dynamic image to the static image.
 13. Anapparatus as defined in claim 12 wherein the images are indicative ofobjects of interest, the apparatus further comprising display means fordisplaying the objects of interest.
 14. A system for image acquisition,comprising: a processor; an imaging adapter in signal communication withthe processor for receiving image data from each of a static imagingdevice and a dynamic imaging device; and a homography unit in signalcommunication with the processor for computing a planar homographybetween the static and dynamic image data.
 15. A system as defined inclaim 14, further comprising a trajectory unit in signal communicationwith the processor for projecting the trajectory of an object indicatedby at least one of the static and dynamic image data.
 16. A system asdefined in claim 15, further comprising a display adapter in signalcommunication with the processor for displaying the object indicated byat least one of the static and dynamic image data.
 17. A system asdefined in claim 16, further comprising a user interface adapter forchecking the image quality.
 18. A system as defined in claim 14 whereinthe image is indicative of a vehicle.
 19. A program storage devicereadable by machine, tangibly embodying a program of instructionsexecutable by the machine to perform program steps for imageacquisition, the program steps comprising: receiving an image from astatic imaging device; receiving an image from a dynamic imaging device;and registering the dynamic image to the static image using planarhomography.
 20. A device as defined in claim 19, the program stepsfurther comprising: detecting an object of interest in at least one ofthe static and dynamic images; projecting a trajectory for the object ofinterest; and scheduling the dynamic imaging device in response to thetrajectory.
 21. A device as defined in claim 19, the program stepsfurther comprising: detecting an object of interest in at least one ofthe static and dynamic images; and controlling the dynamic imagingdevice in response to the object.
 22. A device as defined in claim 19,the program steps further comprising: detecting an object of interest inat least one of the static and dynamic images; and storing each of thestatic and dynamic images in association with a timestamp.
 23. A deviceas defined in claim 19, the program steps further comprising: detectingan object of interest in at least one of the static and dynamic images;and controlling the dynamic imaging device in response to acquisitioncriteria.